Metadata-Version: 2.1
Name: futureframe
Version: 0.1.9
Summary: Accelerate your data science workflow from months to days with foundation models for tabular data.
Home-page: https://futureframe.ai
Keywords: data science,foundation models,tabular data
Author: Future Frame
Maintainer: Eduardo Dadalto
Maintainer-email: eduardo@futureframe.ai
Requires-Python: >=3.10,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: fire (>=0.6.0,<0.7.0)
Requires-Dist: numpy (>=1.17,<2.0)
Requires-Dist: openml (>=0.14.2,<0.15.0)
Requires-Dist: pandas (>=2.2.2,<3.0.0)
Requires-Dist: pyarrow (>=16.1.0,<17.0.0)
Requires-Dist: pydantic (>=2.7.2,<3.0.0)
Requires-Dist: scikit-learn (>=1.4.2,<2.0.0)
Requires-Dist: torch (>=2,<3)
Requires-Dist: transformers (>=4.41.0,<5.0.0)
Project-URL: Documentation, https://futureframe.ai/docs
Project-URL: Repository, https://github.com/futureframeai/futureframe
Description-Content-Type: text/markdown

# 💠 Future Frame

<p align="center">
  <i>Accelerate your data science workflow from months to days with foundation models for tabular data</i>
</p>

## Installation

```bash
pip install futureframe
```

## Quick-start guide

```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

import futureframe as ff

dataset_name = "tests/data/churn.csv"
target_variable = "Churn"
df = pd.read_csv(dataset_name)

X, y = df.drop(columns=[target_variable]), df[target_variable]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

##############
# Future Frame
##############
model = ff.models.CM2Classifier()
model.finetune(X_train, y_train)

y_pred = model.predict(X_test)
##############

auc = roc_auc_score(y_test, y_pred)
print(f"AUC: {auc:0.2f}")
```

## Models

| Model Name | Paper Title                                                | Paper                                               | GitHub                                 |
| ---------- | ---------------------------------------------------------- | --------------------------------------------------- | -------------------------------------- |
| CM2        | Towards Cross-Table Masked Pretraining for Web Data Mining | [Ye et al., 2024](https://arxiv.org/abs/2307.04308) | [Link](https://github.com/Chao-Ye/CM2) |

More to come!

## Important links

- [Future Frame Website](https://futureframe.ai/)
- [`futureframe` Pypi package index](https://pypi.python.org/pypi/futureframe)
- [`futureframe` Github repository](https://github.com/futureframeai/futureframe)
<!-- - [Documentation](https://futureframe.ai/docs/) -->

## Contributing

We are currently under heavy development. If you want to contribute, please send us an email at <i>eduardo(at)futureframe.ai</i>.

If you find any bugs, please write us an [issue](https://github.com/futureframeai/futureframe/issues/new) :).

